This proposal requests funding for the Validation Phase of a project whose ultimate goal is to identify occlusal configurations that impact negatively on social-psychological well-being. The main attribute of dental appearance to which both the public and the individual respond is dental aesthetics. A research tool, or index, that selects out and measures only the aesthetic component of occlusal conditions is needed to objectively differentiate individuals on the basis of social norms for dental aesthetics. Our goal is to provide a reliable and valid dental aesthetic index that could be used by researchers examining issues such as stereotyping based on dento-facial attractiveness, the impact of dental aesthetics on social-psychological functioning in educational and occupational settings, and the demand for and effects of orthodontic treatment. Our preliminary research has provided a regression equation that links objective measurements of occlusal morphology with societal norms for dental appearance. Our specific aim in this proposal is to cross-validate the regression equation. Cross-validation is necessary to establish the general applicability of the regression equation. To cross-validate the regression equation, we must replicate our preliminary research using another similarly drawn sample of 100 occlusal configurations to be rated by the public for dental aesthetics. Then, using this data, a second regression equation will be developed to be cross-validated against the first. In its simplest form, cross-validation consists of 1) deriving a multiple regression equation for a calibration sample and 2) applying the same equation to a second, or validation, sample. The equation's goodness-of-fit in the validation sample is used as a measure of its general applicability. For this research, a double cross-validation procedure is proposed in which two regression equations are developed initially. Then each equation is cross-validated on the other's sample. If both equations are judged reasonably precise estimates and the corresponding regression coefficients are of similar magnitude, the data are merged and a new, more refined equation is computed from the combined samples. In essence, the cross-validated regression equation will specify which physical trait measurements must be made on each patient, as well as specifying how these traits should be differentially weighted and combined to obtain an index score.